A Novel Framework for Gauging Information Extracted from Smartphones Using Neutrosophic Logic

Abstract

Smartphones contain a vast amount of information about their users, which can be used as 
evidence in criminal cases. However, the sheer volume of data can make it challenging for forensic 
investigators to identify and use the most relevant information. Neutrosophic logic is a 
generalization of fuzzy logic that allows for uncertainty and vagueness, making it a more potent tool 
for dealing with the ambiguity of real-world data. The proposed framework aims to identify and 
utilize the most relevant information for forensic investigators, making it easier to solve criminal 
cases  using Neutrosophic logic. This novel approach provides a more powerful tool for dealing with 
the ambiguity of smartphone data, ultimately improving the accuracy and effectiveness of forensic 
investigations. Our research has utilized Neutrosophic logic to evaluate the degree of truth, falsity, 
or Indeterminacy of this information. Additionally, this study analyzes conversations between 
individuals using Excel’s fuzzy lookup add-in to determine the percentage of truth and false in each 
conversation. The results were compiled into a dataset and utilized a Neutrosophic Python code to 
evaluate the information. The results indicate the percentage of truth, false, and Indeterminacy in 
each conversation and which can be used to determine its admissibility as evidence and which not. 

DOI: 10.5281/zenodo.13988374